On Fast Approximate Submodular Minimization

2011

Conference Paper

ei

We are motivated by an application to extract a representative subset of machine learning training data and by the poor empirical performance we observe of the
popular minimum norm algorithm. In fact, for our application, minimum norm can have a running time of about O(n7) (O(n5) oracle calls). We therefore propose a fast approximate method to minimize arbitrary submodular functions. For a large sub-class of submodular functions, the algorithm is exact. Other submodular functions are iteratively approximated by tight submodular upper bounds, and then repeatedly optimized. We show theoretical properties, and empirical results suggest significant speedups over minimum norm while retaining higher accuracies.

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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems